1. Field of the Invention
The present invention relates to a method of reducing block noise, mosquito noise and other noises in an image, which noises are caused at the time of decoding encoded, compressed image data on a block-by-block.
2. Related Art
In order to produce a highly compressed image data, an orthogonally transformation encoding as a highly effective image data compression technique is generally employed. This technique in summary involves dividing an input image signal into blocks of such as 8×8 pixels by a blocking circuit, subjecting the divided blocks to an orthogonal transformation process by an orthogonal transformation circuit to generate a frequency component signal, subjecting the orthogonally transformed data to a linear quantization process by a quantization circuit using a predetermined quantization step width, and allocating a variable length code to the result of the quantization by a variable-length encoding circuit to generate an encoded image signal. This orthogonal transform includes such as discrete Fourier transform, Walsh-Hadamard transform, Karhunen-Loevel transform and discrete cosine transform (DCT). Of them, the DCT is most widely used.
On the other hand, in a decoding apparatus for regenerating an image signal corresponding to the input image signal from the encoded image signal generated by the above compression encoding apparatus, the encoded image signal is subjected to variable length decoding by a variable-length decoding circuit, and reverse quantization is performed by a reverse-quantization circuit using a predetermined quantization step width and the image signal is regenerate by reverse orthogonal transform.
Herein, the orthogonal transform and the quantization which are both nonreversible transform contain errors in the regenerated image signal obtained by the decoding apparatus. Particularly, quantization errors in quantization and reverse quantization deteriorate the quality of the regenerated image. The larger the quantization step width (or the larger the compression rate), the greater the number of quantization errors caused and hence the more noticeable the deterioration of the quality of the regenerated image signal. Image deteriorations inherent to such orthogonal transform include block noise and mosquito noise, the former resulting from discontinuity along the boundary of adjacent blocks which is perceived like a mosaic appearance, the latter being perceived like swarms of mosquitoes clustered around a contour of a character or a figure on the background of the image (hereinafter referred simply to “a contour”), due to encoding and decoding on a block-by-block.
In general, these image noises are reduced by filtering image data and then smoothing the same, as described in Japanese Patent Application Laid-open Nos. 1996-205157, 1997-186993, 1998-191335, 1998-164576 and 1999-317943. These image noise reducing processes are in certain senses achieved by deterioration of an original image, thus causing an adverse effect of lowering the quality of an image to a greater or lesser extent or blurring an image. Therefore, it is general to set the extent or intensity of the image noise reduction in a filtering process so as to balance the effect of the image noise reduction and the influence of the image deterioration by the filtering process.
Meanwhile, when an image is outputted to output media such as by printing out a photo using a photo-processing apparatus, or printing out an image using a printer of an ink jet recording type or thermal transfer recording type, a variety of output sizes such as so-called L-size (127 mm×89 mm), 2 L-size (127 mm×178 mm) and 4 L-size (127 mm×254 mm) are appropriately selected so that an image data is subjected to a data expansion/reduction process according to the output size as selected. This data expansion/reduction process is performed by applying a given calculation (a kind of filtering process) to pixel data of each pixel and its peripheral pixels. These image noise reducing process and data expansion/reduction process are independently made according to their different purposes and therefore any consideration on mutual influences or interdependences are not taken into account.
According to such independent processing, when output media is to be observed, the distance between the output media and an observer (an observation distance) is varied depending on the output size. More specifically, the smaller the output size, the shorter the observation distance; and the larger the output size, the longer the observation distance. For example, in comparison between the output media of the L-size and the output media of 4 L-size, the output media of the 4-L size is observed from a far distance than the output media of the L-size is observed.
That is, as described above, according to a conventional image noise reducing method that performs a constant filtering process irrespective of the output size (i.e., a conventional image noise reducing method designed based on the assumption that output media is observed from a constant distance), the extent of the image noise reduction tends to be visually perceived (hereinafter referred simply as “perceived”) as significant or an image tends to be perceived as blurred for a small output size since it is observed from a near distance. On the other hand, the extent of the image noise reduction tends to be perceived as insignificant or an image tends to be perceived as an image with insufficient noise reduction applied thereto or with image noises insufficiently reduced for a large output size. Thus, a problem arises that even the same image may be differently perceived depending on the output size.
In consideration of the above problem, it is an object of the present invention to provide a method of reducing noise in images that is capable of allowing the extent of the image noise reduction applied to image data to be perceived in a similar fashion, irrespective of the output size.